Overview of SnakeCLEF 2024: Revisiting Snake Species Identification in Medically Important Scenarios

Abstract

The SnakeCLEF challenge serves as a major benchmark for evaluating the performance of AI-driven methods in snake species recognition on a global scale. The 5th edition of the SnakeCLEF challenge builds on last year's training data and extends the test set with new data from private collections originating from southern Africa. Similar to last year, SnakeCLEF 2024 focuses on (i) evaluating incremental improvements in automatic snake species identification, (ii) testing global generalization in three specific scenarios: India, Central America, and southern Africa, and (iii) assessing the impact of uneven error costs, such as mistaking a venomous snake for a harmless one. In this paper, we highlight the crucial importance of a robust automatic snake identification system, especially in resource-limited environments and in neglected regions, and its potential benefits for biodiversity conservation and global health. We present (i) a detailed description of the provided data, (ii) the evaluation methodology, (iii) an overview of the submitted methods, and (iv) insights gained from the results. © 2024 Copyright for this paper by its authors.